{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyONXDbdHcDSu1Tn9A+/sqWg",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aleksandrkaplun/AmazonStockPredict/blob/main/AmazonStock.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "qXrIDSLblODn"
      },
      "outputs": [],
      "source": [
        "#import libraries\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import time\n",
        "import seaborn as sns\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Read dataset and plotting amazon stock prices\n",
        "data = pd.read_csv('AMZN_2006-01-01_to_2018-01-01.csv')\n",
        "data.head()\n",
        "plt.plot(data[['Close']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "5WbgbkrQidc1",
        "outputId": "f3a1ba96-4968-4f94-b3b2-d87bc1454963"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7a2ce282ac20>]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "price = data[['Close']]\n",
        "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
        "price['Close'] = scaler.fit_transform(price['Close'].values.reshape(-1,1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "joDj3VUzieoy",
        "outputId": "22d20b33-476b-416e-bbfa-98d2c1c26771"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-3-fa848fa03d6c>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  price['Close'] = scaler.fit_transform(price['Close'].values.reshape(-1,1))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def split_data(stock, lookback):\n",
        "  data_raw = stock.to_numpy() # convert to numpy array\n",
        "  data = []\n",
        "  # create all possible sequences of length seq_len\n",
        "  for index in range(len(data_raw) - lookback):\n",
        "   data.append(data_raw[index: index + lookback])\n",
        "  data = np.array(data);\n",
        "  test_set_size = int(np.round(0.2*data.shape[0]));\n",
        "  train_set_size = data.shape[0] - (test_set_size);\n",
        "  x_train = data[:train_set_size,:-1,:]\n",
        "  y_train = data[:train_set_size,-1,:]\n",
        "  x_test = data[train_set_size:,:-1]\n",
        "  y_test = data[train_set_size:,-1,:]\n",
        "  return [x_train, y_train, x_test, y_test]\n",
        "lookback = 10 # choose sequence length\n",
        "x_train, y_train, x_test, y_test = split_data(price, lookback)"
      ],
      "metadata": {
        "id": "P4nLvhteiqPW"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "x_train = torch.from_numpy(x_train).type(torch.Tensor)\n",
        "x_test = torch.from_numpy(x_test).type(torch.Tensor)\n",
        "y_train_lstm = torch.from_numpy(y_train).type(torch.Tensor)\n",
        "y_test_lstm = torch.from_numpy(y_test).type(torch.Tensor)\n",
        "y_train_gru = torch.from_numpy(y_train).type(torch.Tensor)\n",
        "y_test_gru = torch.from_numpy(y_test).type(torch.Tensor)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "id": "FrIq5OUDiyKv",
        "outputId": "05c7ae17-3eee-4453-e68f-83fca1fbb7ef"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "expected np.ndarray (got Tensor)",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-76414cd1ba36>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mx_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0my_train_lstm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0my_test_lstm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0my_train_gru\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: expected np.ndarray (got Tensor)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# set parametrs\n",
        "input_dim = 1\n",
        "hidden_dim = 32\n",
        "num_layers = 2\n",
        "output_dim = 1\n",
        "num_epochs = 100"
      ],
      "metadata": {
        "id": "IOKag11Di6VX"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class LSTM(nn.Module):\n",
        "   def __init__(self, input_dim, hidden_dim, num_layers, output_dim):\n",
        "    super(LSTM, self).__init__()\n",
        "    self.hidden_dim = hidden_dim\n",
        "    self.num_layers = num_layers\n",
        "    self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)\n",
        "    self.fc = nn.Linear(hidden_dim, output_dim)\n",
        "   def forward(self, x):\n",
        "     h0 = torch.zeros(self.num_layers, x.size(0),\n",
        "     self.hidden_dim).requires_grad_()\n",
        "     c0 = torch.zeros(self.num_layers, x.size(0),\n",
        "     self.hidden_dim).requires_grad_()\n",
        "     out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))\n",
        "     out = self.fc(out[:, -1, :])\n",
        "     return out"
      ],
      "metadata": {
        "id": "ltoY6ytvi-ue"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LSTM(input_dim=input_dim, hidden_dim=hidden_dim,\n",
        "output_dim=output_dim, num_layers=num_layers)\n",
        "criterion = torch.nn.MSELoss(reduction='mean')\n",
        "optimiser = torch.optim.Adam(model.parameters(), lr=0.01)"
      ],
      "metadata": {
        "id": "cxL3WCaqjG82"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hist = np.zeros(num_epochs)\n",
        "start_time = time.time()\n",
        "lstm = []\n",
        "for t in range(num_epochs):\n",
        "   y_train_pred = model(x_train)\n",
        "   loss = criterion(y_train_pred, y_train_lstm)\n",
        "   print(\"Epoch \", t, \"MSE: \", loss.item())\n",
        "   hist[t] = loss.item()\n",
        "   optimiser.zero_grad()\n",
        "   loss.backward()\n",
        "   optimiser.step()\n",
        "training_time = time.time()-start_time\n",
        "print(\"Training time: {}\".format(training_time))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r-gYSX_0jMOQ",
        "outputId": "d74a097e-98aa-45a4-8404-a7b4323af4a2"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch  0 MSE:  0.31887102127075195\n",
            "Epoch  1 MSE:  0.18240021169185638\n",
            "Epoch  2 MSE:  0.07359597086906433\n",
            "Epoch  3 MSE:  0.04506872594356537\n",
            "Epoch  4 MSE:  0.08014322817325592\n",
            "Epoch  5 MSE:  0.04423438385128975\n",
            "Epoch  6 MSE:  0.034213412553071976\n",
            "Epoch  7 MSE:  0.04279409721493721\n",
            "Epoch  8 MSE:  0.04997475445270538\n",
            "Epoch  9 MSE:  0.05008243769407272\n",
            "Epoch  10 MSE:  0.044780220836400986\n",
            "Epoch  11 MSE:  0.03785248473286629\n",
            "Epoch  12 MSE:  0.03314775601029396\n",
            "Epoch  13 MSE:  0.03293933719396591\n",
            "Epoch  14 MSE:  0.03618716448545456\n",
            "Epoch  15 MSE:  0.038878146559000015\n",
            "Epoch  16 MSE:  0.037943560630083084\n",
            "Epoch  17 MSE:  0.03423905745148659\n",
            "Epoch  18 MSE:  0.030717983841896057\n",
            "Epoch  19 MSE:  0.029241137206554413\n",
            "Epoch  20 MSE:  0.029597412794828415\n",
            "Epoch  21 MSE:  0.030407298356294632\n",
            "Epoch  22 MSE:  0.030317086726427078\n",
            "Epoch  23 MSE:  0.028708064928650856\n",
            "Epoch  24 MSE:  0.025907136499881744\n",
            "Epoch  25 MSE:  0.02301771193742752\n",
            "Epoch  26 MSE:  0.021295851096510887\n",
            "Epoch  27 MSE:  0.020951639860868454\n",
            "Epoch  28 MSE:  0.020217204466462135\n",
            "Epoch  29 MSE:  0.01707453653216362\n",
            "Epoch  30 MSE:  0.012555270455777645\n",
            "Epoch  31 MSE:  0.009660213254392147\n",
            "Epoch  32 MSE:  0.00850428082048893\n",
            "Epoch  33 MSE:  0.005401465110480785\n",
            "Epoch  34 MSE:  0.0013465248048305511\n",
            "Epoch  35 MSE:  0.00304638990201056\n",
            "Epoch  36 MSE:  0.002623375505208969\n",
            "Epoch  37 MSE:  0.004663511179387569\n",
            "Epoch  38 MSE:  0.005834858864545822\n",
            "Epoch  39 MSE:  0.0038750660605728626\n",
            "Epoch  40 MSE:  0.00378301995806396\n",
            "Epoch  41 MSE:  0.0013277392135933042\n",
            "Epoch  42 MSE:  0.0009645487880334258\n",
            "Epoch  43 MSE:  0.001474616234190762\n",
            "Epoch  44 MSE:  0.001008139457553625\n",
            "Epoch  45 MSE:  0.0010766887571662664\n",
            "Epoch  46 MSE:  0.0018389015458524227\n",
            "Epoch  47 MSE:  0.002047321991994977\n",
            "Epoch  48 MSE:  0.0017745312070474029\n",
            "Epoch  49 MSE:  0.0017893651966005564\n",
            "Epoch  50 MSE:  0.0018677468178793788\n",
            "Epoch  51 MSE:  0.001487946487031877\n",
            "Epoch  52 MSE:  0.0009870589710772038\n",
            "Epoch  53 MSE:  0.0008623564499430358\n",
            "Epoch  54 MSE:  0.0007539920043200254\n",
            "Epoch  55 MSE:  0.00043950299732387066\n",
            "Epoch  56 MSE:  0.0004634910146705806\n",
            "Epoch  57 MSE:  0.0007061588112264872\n",
            "Epoch  58 MSE:  0.0006784948636777699\n",
            "Epoch  59 MSE:  0.0007456571911461651\n",
            "Epoch  60 MSE:  0.0009079379378817976\n",
            "Epoch  61 MSE:  0.0007500378997065127\n",
            "Epoch  62 MSE:  0.0006226167897693813\n",
            "Epoch  63 MSE:  0.0006062827887944877\n",
            "Epoch  64 MSE:  0.0004483454395085573\n",
            "Epoch  65 MSE:  0.0003642857482191175\n",
            "Epoch  66 MSE:  0.0004269461496733129\n",
            "Epoch  67 MSE:  0.0004276046238373965\n",
            "Epoch  68 MSE:  0.00042716690222732723\n",
            "Epoch  69 MSE:  0.0005023104604333639\n",
            "Epoch  70 MSE:  0.0005198303260840476\n",
            "Epoch  71 MSE:  0.0004737447015941143\n",
            "Epoch  72 MSE:  0.0004665597516577691\n",
            "Epoch  73 MSE:  0.00044773874105885625\n",
            "Epoch  74 MSE:  0.0003799292608164251\n",
            "Epoch  75 MSE:  0.0003546419902704656\n",
            "Epoch  76 MSE:  0.0003609524283092469\n",
            "Epoch  77 MSE:  0.0003405763127375394\n",
            "Epoch  78 MSE:  0.00034893659176304936\n",
            "Epoch  79 MSE:  0.000380814220989123\n",
            "Epoch  80 MSE:  0.0003754217177629471\n",
            "Epoch  81 MSE:  0.0003730950993485749\n",
            "Epoch  82 MSE:  0.0003767930902540684\n",
            "Epoch  83 MSE:  0.0003504728665575385\n",
            "Epoch  84 MSE:  0.00033350230660289526\n",
            "Epoch  85 MSE:  0.0003315270587336272\n",
            "Epoch  86 MSE:  0.0003185542009305209\n",
            "Epoch  87 MSE:  0.00031862995820119977\n",
            "Epoch  88 MSE:  0.0003293985209893435\n",
            "Epoch  89 MSE:  0.00032763471244834363\n",
            "Epoch  90 MSE:  0.0003283685364294797\n",
            "Epoch  91 MSE:  0.00033153704134747386\n",
            "Epoch  92 MSE:  0.0003222911909688264\n",
            "Epoch  93 MSE:  0.00031498764292337\n",
            "Epoch  94 MSE:  0.0003123764181509614\n",
            "Epoch  95 MSE:  0.00030447146855294704\n",
            "Epoch  96 MSE:  0.00030213111313059926\n",
            "Epoch  97 MSE:  0.0003046805504709482\n",
            "Epoch  98 MSE:  0.0003028310602530837\n",
            "Epoch  99 MSE:  0.0003038788854610175\n",
            "Training time: 16.594590663909912\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "predict = pd.DataFrame(scaler.inverse_transform(y_train_pred.detach().numpy()))\n",
        "original = pd.DataFrame(scaler.inverse_transform(y_train_lstm.detach().numpy()))\n",
        "y_test_pred = model(x_test)\n",
        "\n",
        "test_predict = pd.DataFrame(scaler.inverse_transform(y_test_pred.detach().numpy()))\n",
        "test_original = pd.DataFrame(scaler.inverse_transform(y_test_pred.detach().numpy()))\n",
        "\n",
        "test_predict.values\n",
        "test_original.values\n",
        "\n",
        "np.max(np.abs(test_predict.values-test_original.values))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VdQ3H2PMjafs",
        "outputId": "354dbd01-9969-45d5-ec6b-adfce548aca4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_style(\"darkgrid\")\n",
        "fig = plt.figure()\n",
        "fig.subplots_adjust(hspace=0.2, wspace=0.2)\n",
        "plt.subplot(1, 2, 1)\n",
        "ax = sns.lineplot(x = original.index, y = original[0],\n",
        "label=\"Data\", color='royalblue')\n",
        "ax = sns.lineplot(x = predict.index, y = predict[0],\n",
        "label=\"Training Prediction (LSTM)\", color='tomato')\n",
        "ax.set_title('Stock price', size = 14, fontweight='bold')\n",
        "ax.set_xlabel(\"Days\", size = 14)\n",
        "ax.set_ylabel(\"Cost (USD)\", size = 14)\n",
        "ax.set_xticklabels('', size=10)\n",
        "plt.subplot(1, 2, 2)\n",
        "ax = sns.lineplot(data=hist, color='royalblue')\n",
        "ax.set_xlabel(\"Epoch\", size = 14)\n",
        "ax.set_ylabel(\"Loss\", size = 14)\n",
        "ax.set_title(\"Training Loss\", size = 14, fontweight='bold')\n",
        "fig.set_figheight(6)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "id": "dkD8CR7Vjqkm",
        "outputId": "3100576f-6c4e-44fc-c2c8-5e1cbeedc395"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}